This winner in 2014 is Prof. Pedro Domingos from U. Washington. The SIGKDD Innovation Award recognizes outstanding technical contributions to the field of knowledge discovery in data and data mining that have had a lasting impact. This award recognizes Pedro Domingos’s work in data stream analysis, cost-sensitive classification, adversarial learning, and Markov logic networks, as well as applications in viral marketing and information integration.

Ted Senator is the winner of the 2014 Service Award. The Service Award recognizes individuals for their outstanding service contributions to the field of knowledge discovery in the community. Senator earned this award for his work influencing the direction of major conferences, helping define the distinction between research and applications in knowledge discovery, and highlighting the challenges specific to the field for outside entities.

The three Test of Time Awards are granted to papers from past KDD conferences beyond the last decade that have had a profound influence on the data mining research community. The winning papers are as follows:

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, by Martin Ester, Hans-Peter Kriegel, Jiirg Sander, and Xiaowei Xu [KDD 1996]. This paper introduced density-based clustering to the data mining community. Since its introduction, density-based clustering has become one of the prominent clustering paradigms. Since the publication of DBSCAN, density-based clustering has been extensively studied and has been successfully used in many applications.

Integrating Classification and Association Rule Mining, by Bing Liu, Wynne Hsu, and Yiming Ma [KDD 1998]. This paper pioneered the research of using association rules for classification by integrating classification and association rule mining. It also proposed an efficient algorithm and built the first system (called CBA) for the purpose. This work triggered a large number of follow-up works and applications.

Maximizing the Spread of Influence through a Social Network, by David Kempe, Jon Kleinberg, and Eva Tardos [KDD 2003]. This paper considers questions involving the spread of information, innovations, and behaviors in social networks. The paper identifies a technically rich structure inherent in the problem, and establishes a framework that has subsequently been used in areas ranging from social media and marketing to the diffusion of innovations and the study of inequality.

The overall best research track paper was Reducing the Sampling Complexity of Topic Models, by Aaron Q Li, Amr Ahmed, Sujith Ravi, and Alexander J Smola, which presents an approximate sampler for topic models that theoretically and experimentally outperforms existing samplers thereby allowing topic models to scale to industry-scale datasets.

The best student paper was An Efficient Algorithm For Weak Hierarchical Lasso, by Yashu Liu, Jie Wang, and Jieping Ye, which presents algorithms for tackling the non-convexity that arises in using the hierarchical lasso when regularizing parameters of models that attempt to capture non-linear feature interaction.

Best Best Industry and Government Track PaperStyle in the Long Tail: Discovering Unique Interests with Latent Variable Models in Large Scale Social E-commerce
by Diane J. Hu, Rob Hall, and Josh Attenberg (Etsy)